{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "directorys=['LTH_real_plot']  # temp models\n",
    "for directory in directorys:\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "result=np.load('result.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=result.reshape(25,int(len(result)/25))\n",
    "MaskLow,MaskUp,Maskmean,Ntr=[],[],[],[]\n",
    "for j in range(25):\n",
    "    i=4\n",
    "    MaskLow.append(np.mean(df[i*5:i*5+5,j])-min(df[i*5:i*5+5,j]))\n",
    "    MaskUp.append(max(df[i*5:i*5+5,j])-np.mean(df[i*5:i*5+5,j]))\n",
    "    #FCLow.append(np.mean(df[i*5:i*5+5,2])-min(df[i*5:i*5+5,2]))\n",
    "    #FCUp.append(max(df[i*5:i*5+5,2])-np.mean(df[i*5:i*5+5,2]))\n",
    "    Maskmean.append(np.mean(df[i*5:i*5+5,j]))\n",
    "    #FCmean.append(np.mean(df[i*5:i*5+5,2]))\n",
    "    Ntr.append(0.8**j*100)\n",
    "Maskerr= np.concatenate((np.array(MaskLow).reshape(1,len(MaskLow)),np.array(MaskUp).reshape(1,len(MaskUp))),axis=0)\n",
    "    #FCerr=np.concatenate((np.array(FCLow).reshape(1,len(FCLow)),np.array(FCUp).reshape(1,len(FCUp))),axis=0)\n",
    "fig, ax = plt.subplots()\n",
    "#ax.errorbar(Ntr, FCmean, xerr=100, yerr=FCerr,capthick=4)\n",
    "ax.errorbar(Ntr, Maskmean, xerr=0, yerr=Maskerr)\n",
    "    #plt.legend(['OverparaFC','ParaACE'])\n",
    "plt.xlabel('Percents of Weights Remaining')\n",
    "plt.ylabel('Test NRMSE')   \n",
    "plt.title('Cal housing, LTH') \n",
    "plt.xscale('log')\n",
    "ax.invert_xaxis()\n",
    "plt.xticks([0.5,1,5,20,50,100],[0.5,1,5,20,50,100])\n",
    "plt.savefig('LTH_real_plot/LTHreal'+str(i+1)+'.pdf',bbox_inches='tight',dpi='figure',pad_inches=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.052263625\n",
      "0.017995846\n",
      "0.12277806\n",
      "0.040380705\n",
      "0.10383181\n"
     ]
    }
   ],
   "source": [
    "for dataset in range(5):\n",
    "    print(np.mean(df[dataset*5:dataset*5+5,20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0016677079\n",
      "0.016660325\n",
      "0.026643533\n",
      "0.0017531057\n",
      "0.00056561443\n"
     ]
    }
   ],
   "source": [
    "for dataset in range(5):\n",
    "    print(np.std(df[dataset*5:dataset*5+5,20]))"
   ]
  }
 ],
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